A test for treatment effects in randomized controlled trials, harnessing the power of ultrahigh dimensional big data.

ABSTRACT:

Background

The randomized controlled trial (RCT) is the gold-standard research design in biomedicine. However, practical concerns often limit the sample size, n, the number of patients in a RCT. We aim to show that the power of a RCT can be increased by increasing p, the number of baseline covariates (sex, age, socio-demographic, genomic, and clinical profiles et al, of the patients) collected in the RCT (referred to as the 'dimension').

Methods

The conventional test for treatment effects is based on testing the 'crude null' that the outcomes of the subjects are of no difference between the two arms of a RCT. We propose a 'high-dimensional test' which is based on testing the 'sharp null' that the experimental intervention has no treatment effect whatsoever, for patients of a

SUBMITTER: Lee WC 

PROVIDER: S-EPMC6824789 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature